Abstract

Abstract Summary: A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms. Availability: http://genome.tugraz.at Contact: zlatko.trajanoski@tugraz.at * To whom correspondence should be addressed.

Keywords

Cluster analysisNormalization (sociology)Hierarchical clusteringComputer scienceData miningPrincipal component analysisVisualizationJavaMicroarray analysis techniquesSuiteMicroarray databasesClustering high-dimensional dataGene chip analysisDNA microarrayArtificial intelligenceBiologyGeneGene expressionGenetics

Affiliated Institutions

Related Publications

Publication Info

Year
2002
Type
article
Volume
18
Issue
1
Pages
207-208
Citations
1782
Access
Closed

External Links

View on DOI.org

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

1782
OpenAlex

Cite This

Alexander Sturn, John Quackenbush, Zlatko Trajanoski (2002). Genesis: cluster analysis of microarray data. Bioinformatics , 18 (1) , 207-208. https://doi.org/10.1093/bioinformatics/18.1.207

Identifiers

DOI
10.1093/bioinformatics/18.1.207